{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f1cba2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      " False  True  True  True  True  True  True  True  True False  True  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True False  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True False  True  True  True  True  True\n",
      " False False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      " False  True  True  True  True  True  True  True  True  True False  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True False  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True]\n",
      "准确率为：\n",
      " 0.9422222222222222\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51  0  1  0  0  0  0  0  0  0]\n",
      " [ 0 38  0  0  0  0  0  0  2  2]\n",
      " [ 0  0 50  1  0  0  0  0  0  0]\n",
      " [ 0  0  2 36  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 50  0  0  0  0  1]\n",
      " [ 0  0  0  0  0 50  0  0  0  1]\n",
      " [ 0  1  0  0  0  0 43  0  1  0]\n",
      " [ 0  0  0  0  1  0  0 43  0  0]\n",
      " [ 0  0  2  0  0  0  1  1 34  1]\n",
      " [ 0  1  0  1  0  2  0  0  2 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        52\n",
      "           1       0.95      0.90      0.93        42\n",
      "           2       0.91      0.98      0.94        51\n",
      "           3       0.95      0.90      0.92        40\n",
      "           4       0.98      0.98      0.98        51\n",
      "           5       0.96      0.98      0.97        51\n",
      "           6       0.98      0.96      0.97        45\n",
      "           7       0.98      0.98      0.98        44\n",
      "           8       0.85      0.87      0.86        39\n",
      "           9       0.83      0.83      0.83        35\n",
      "\n",
      "    accuracy                           0.94       450\n",
      "   macro avg       0.94      0.94      0.94       450\n",
      "weighted avg       0.94      0.94      0.94       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def Logistic_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=10000)  # 可以选择多分类方法 ovr 和 multinomial\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    Logistic_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec1851f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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